from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match

print_time_report()

Time report

sklearnex_KMeans_short: 0h 0m 1s

sklearnex_Ridge: 0h 0m 1s

KMeans_short: 0h 0m 2s

sklearnex_LogisticRegression: 0h 0m 4s

sklearnex_KMeans_tall: 0h 0m 7s

Ridge: 0h 0m 11s

LogisticRegression: 0h 0m 20s

KMeans_tall: 0h 0m 22s

sklearnex_KNeighborsClassifier_kd_tree: 0h 0m 30s

sklearnex_KNeighborsClassifier: 0h 2m 27s

KNeighborsClassifier_kd_tree: 0h 2m 44s

catboost_symmetric: 0h 5m 3s

catboost_lossguide: 0h 5m 5s

lightgbm: 0h 5m 9s

HistGradientBoostingClassifier: 0h 5m 19s

xgboost: 0h 5m 25s

KNeighborsClassifier: 0h 33m 52s

total: 1h 6m 52s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.8.0-1036-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.3",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.21.0",
    "scipy": "1.7.0",
    "Cython": null,
    "pandas": "1.3.0",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.158	0.0	5.060	0.0	1	1	NaN	NaN	0.490	0.0	0.323	0.0	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.133	0.0	6.029	0.0	-1	100	NaN	NaN	0.478	0.0	0.278	0.0	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.129	0.0	6.187	0.0	-1	1	NaN	NaN	0.477	0.0	0.271	0.0	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.131	0.0	6.113	0.0	1	5	NaN	NaN	0.473	0.0	0.276	0.0	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.127	0.0	6.300	0.0	-1	5	NaN	NaN	0.473	0.0	0.268	0.0	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.128	0.0	6.239	0.0	1	100	NaN	NaN	0.472	0.0	0.272	0.0	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.0	0.285	0.0	1	1	NaN	NaN	0.101	0.0	0.555	0.0	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.0	0.295	0.0	-1	100	NaN	NaN	0.101	0.0	0.537	0.0	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.0	0.289	0.0	-1	1	NaN	NaN	0.100	0.0	0.552	0.0	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.071	0.0	0.226	0.0	1	5	NaN	NaN	0.101	0.0	0.701	0.0	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.0	0.284	0.0	-1	5	NaN	NaN	0.100	0.0	0.560	0.0	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.0	0.288	0.0	1	100	NaN	NaN	0.101	0.0	0.548	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	12.820	0.099	0.0	0.013	1	1	0.694	0.956	1.778	0.008	7.210	0.064	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.217	0.009	0.0	0.217	1	1	1.000	1.000	0.090	0.001	2.419	0.105	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.312	0.000	0.0	0.035	-1	100	0.926	0.730	1.739	0.024	20.310	0.280	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.184	0.018	0.0	0.184	-1	100	1.000	0.000	0.091	0.001	2.035	0.200	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	26.709	0.885	0.0	0.027	-1	1	0.694	0.796	1.739	0.017	15.359	0.531	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.183	0.014	0.0	0.183	-1	1	1.000	1.000	0.088	0.001	2.073	0.165	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	22.393	0.030	0.0	0.022	1	5	0.793	0.956	1.777	0.008	12.603	0.058	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.212	0.001	0.0	0.212	1	5	1.000	1.000	0.089	0.000	2.366	0.015	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.726	0.000	0.0	0.036	-1	5	0.793	0.796	1.736	0.014	20.576	0.169	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.190	0.016	0.0	0.190	-1	5	1.000	1.000	0.089	0.001	2.142	0.178	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	22.409	0.088	0.0	0.022	1	100	0.926	0.730	1.719	0.005	13.033	0.066	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.215	0.002	0.0	0.215	1	100	1.000	0.000	0.093	0.011	2.298	0.270	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.418	0.049	0.0	0.010	1	1	0.983	0.989	0.303	0.003	34.437	0.374	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.014	0.001	0.0	0.014	1	1	1.000	1.000	0.005	0.000	2.638	0.273	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	32.282	0.000	0.0	0.032	-1	100	0.986	0.978	0.253	0.003	127.689	1.297	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.036	0.003	0.0	0.036	-1	100	1.000	1.000	0.005	0.001	7.128	0.874	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	24.130	0.129	0.0	0.024	-1	1	0.983	0.986	0.254	0.001	95.097	0.587	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.022	0.002	0.0	0.022	-1	1	1.000	1.000	0.005	0.000	4.280	0.577	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	18.805	0.048	0.0	0.019	1	5	0.984	0.989	0.300	0.001	62.601	0.293	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.028	0.000	0.0	0.028	1	5	1.000	1.000	0.005	0.000	5.430	0.528	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	32.068	0.000	0.0	0.032	-1	5	0.984	0.986	0.255	0.002	126.005	0.757	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.035	0.003	0.0	0.035	-1	5	1.000	1.000	0.005	0.000	6.578	0.781	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	18.809	0.062	0.0	0.019	1	100	0.986	0.978	0.256	0.003	73.554	0.767	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.028	0.000	0.0	0.028	1	100	1.000	1.000	0.005	0.000	5.128	0.266	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.422	0.0	0.023	0.0	-1	1	NaN	NaN	0.734	0.0	4.660	0.0	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.846	0.0	0.028	0.0	-1	100	NaN	NaN	0.730	0.0	3.899	0.0	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.886	0.0	0.028	0.0	1	5	NaN	NaN	0.743	0.0	3.885	0.0	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.876	0.0	0.028	0.0	-1	5	NaN	NaN	0.728	0.0	3.952	0.0	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.882	0.0	0.028	0.0	1	1	NaN	NaN	0.722	0.0	3.993	0.0	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.889	0.0	0.028	0.0	1	100	NaN	NaN	0.720	0.0	4.013	0.0	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.732	0.0	0.022	0.0	-1	1	NaN	NaN	0.455	0.0	1.611	0.0	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.793	0.0	0.020	0.0	-1	100	NaN	NaN	0.454	0.0	1.746	0.0	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.773	0.0	0.021	0.0	1	5	NaN	NaN	0.459	0.0	1.685	0.0	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.740	0.0	0.022	0.0	-1	5	NaN	NaN	0.474	0.0	1.560	0.0	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.735	0.0	0.022	0.0	1	1	NaN	NaN	0.479	0.0	1.534	0.0	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.721	0.0	0.022	0.0	1	100	NaN	NaN	0.527	0.0	1.368	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.466	0.007	0.000	0.000	-1	1	0.960	0.974	0.623	0.004	0.748	0.012	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.002	0.001	1.628	0.868	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.832	0.016	0.000	0.003	-1	100	0.974	0.963	0.124	0.004	22.829	0.804	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.001	0.000	0.005	-1	100	1.000	1.000	0.000	0.000	11.056	6.776	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.511	0.038	0.000	0.002	1	5	0.978	0.963	0.124	0.004	12.202	0.518	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	1	5	1.000	1.000	0.000	0.000	3.102	1.957	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.850	0.011	0.000	0.001	-1	5	0.978	0.975	0.217	0.004	3.915	0.085	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.001	0.000	5.377	3.142	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.778	0.016	0.000	0.001	1	1	0.960	0.974	0.618	0.003	1.259	0.026	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.002	0.001	0.625	0.363	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	5.182	0.079	0.000	0.005	1	100	0.974	0.975	0.223	0.003	23.273	0.474	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	1	100	1.000	1.000	0.001	0.000	5.663	3.636	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.022	0.001	0.001	0.000	-1	1	0.979	0.987	0.006	0.001	3.579	0.459	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	18.056	13.336	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.045	0.000	0.000	0.000	-1	100	0.986	0.974	0.001	0.000	59.380	21.696	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	20.507	17.654	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.023	0.001	0.001	0.000	1	5	0.987	0.974	0.001	0.000	29.925	10.680	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.274	4.241	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.001	0.001	0.000	-1	5	0.987	0.985	0.001	0.000	21.037	5.892	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	21.438	18.879	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.021	0.001	0.001	0.000	1	1	0.979	0.987	0.007	0.001	3.132	0.347	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.956	4.467	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.050	0.002	0.000	0.000	1	100	0.986	0.985	0.001	0.000	47.072	15.794	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.630	5.048	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.596	0.0	0.805	0.0	random	NaN	30	NaN	0.372	0.0	1.602	0.0	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.587	0.0	0.817	0.0	k-means++	NaN	30	NaN	0.421	0.0	1.396	0.0	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.435	0.0	3.730	0.0	random	NaN	30	NaN	2.675	0.0	2.406	0.0	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.185	0.0	3.880	0.0	k-means++	NaN	30	NaN	2.820	0.0	2.193	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.359	0.000	random	0.001	30	0.001	0.0	0.0	9.033	6.335	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	random	1.000	30	1.000	0.0	0.0	10.285	8.143	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.356	0.000	k-means++	0.001	30	0.001	0.0	0.0	8.728	6.349	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	k-means++	1.000	30	1.000	0.0	0.0	10.311	7.750	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.002	10.248	0.000	random	0.002	30	0.002	0.0	0.0	8.973	7.764	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.000	0.019	0.001	random	1.000	30	1.000	0.0	0.0	10.012	7.324	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.001	11.755	0.000	k-means++	0.001	30	0.002	0.0	0.0	7.862	5.754	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.000	0.018	0.001	k-means++	1.000	30	1.000	0.0	0.0	9.761	6.716	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	20	0.206	0.0	0.016	0.0	k-means++	NaN	20	NaN	0.083	0.0	2.488	0.0	See	See
3	KMeans_short	fit	10000	10000	2	20	0.072	0.0	0.045	0.0	random	NaN	20	NaN	0.026	0.0	2.703	0.0	See	See
6	KMeans_short	fit	10000	10000	100	20	0.545	0.0	0.294	0.0	k-means++	NaN	20	NaN	0.303	0.0	1.799	0.0	See	See
9	KMeans_short	fit	10000	10000	100	20	0.195	0.0	0.819	0.0	random	NaN	20	NaN	0.106	0.0	1.835	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.188	0.000	k-means++	-0.003	20	-0.000	0.001	0.0	3.370	0.812	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.000	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	10.457	8.111	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.189	0.000	random	0.001	20	0.000	0.001	0.0	3.360	0.964	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.001	0.000	0.001	random	1.000	20	1.000	0.000	0.0	11.859	9.838	See	See
7	KMeans_short	predict	10000	1000	100	20	0.002	0.000	6.779	0.000	k-means++	0.306	20	0.295	0.001	0.0	2.349	0.499	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.000	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	7.838	4.358	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.000	6.798	0.000	random	0.251	20	0.309	0.001	0.0	2.306	0.416	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.000	0.012	0.001	random	1.000	20	1.000	0.000	0.0	8.274	5.685	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	11.358	0.0	[-0.10388143]	0.000	NaN	NaN	NaN	NaN	NaN	1.994	0.0	5.697	0.0	See	See
3	LogisticRegression	fit	1000	1000	10000	[27]	0.840	0.0	[-2.54014548]	0.001	NaN	NaN	NaN	NaN	NaN	0.905	0.0	0.929	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[58.08999374]	0.0	NaN	NaN	NaN	NaN	0.541	0.000	0.0	0.817	0.445	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.2512914]	0.0	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.361	0.383	See	See
4	LogisticRegression	predict	1000	100	10000	[27]	0.002	0.0	[142.86230177]	0.0	NaN	NaN	NaN	NaN	0.350	0.003	0.0	0.527	0.129	See	See
5	LogisticRegression	predict	1000	1	10000	[27]	0.000	0.0	[23.9225176]	0.0	NaN	NaN	NaN	NaN	1.000	0.001	0.0	0.134	0.106	See	See

Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.176	0.0	0.456	0.0	NaN	NaN	NaN	0.177	0.0	0.989	0.0	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.479	0.0	0.541	0.0	NaN	NaN	NaN	0.230	0.0	6.439	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	NaN	0.011	0.0	7.036	0.0	NaN	NaN	0.103	0.019	0.001	0.592	0.042	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	1.032	0.0	NaN	NaN	NaN	0.000	0.000	0.754	0.780	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	5.809	0.0	NaN	NaN	1.000	0.000	0.000	0.635	0.498	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.014	0.0	NaN	NaN	NaN	0.000	0.000	0.663	0.784	See	See